production capacity and capital budgeting for state forest

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Graduate Student Theses, Dissertations, & Professional Papers Graduate School

1984

Production capacity and capital budgeting for state forest Production capacity and capital budgeting for state forest

management planning management planning

Gary R. Schaertl The University of Montana

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COPYRIGHT ACT OF 1976

THIS IS AN UNPUBLISHED MANUSCRIPT IN WHICH COPYRIGHT SUB-STS. ANY FURTHER REPRINTING OF ITS CONTENTS MUST BE APPROVED THE AUTHOR.

MANSFIELD LIBRARY UNIVERSITY JF^I^I^NA

PRODUCTION CAPACITY AND CAPITAL BUDGETING

FOR STATE FOREST MANAGEMENT PLANNING

Professional Paper By

Gary R. Schaertl

B.S. in Forest Management, 1972, Oregon State University

Proposed for Partial Fulfillment of the Requirements for the

Master of Business Administration Degree

UNIVERSITY OF MONTANA

10 August 1984

Approved by:

Richa;rd P. Withycombe, Ph.D. Chairman, Board of Examiners

Raymond C. Murray, Ph, Dean, Graduate School

SJitolH

UMI Number: EP34485

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A1*

Schaertl, Gary R. , MBA, Aug. 10, 1984 Business

Production Capacity and Capital Budgeting for State Forest Management Planning (103pp. 1

Director: Richard P. Withycombe

In this study of the Montana Department of State Lands, financially optimal forest management regimes, rotation ages and a steady-state timber supply schedule were determined for an infinite series of timber crops with the objective of maximizing wealth.

Three computer models were used, PROGNOSIS, SUPPLY and TIMLAN. PROGNOSIS is an individual tree, density independent forest growth and yield model which produces forest stand yields under a range of forest management regimes. SUPPLY and TIMLAN evaluate bare land on a site specific basis for exogenously supplied and embedded model parameters and produce selected management regimes, rotation ages and steady-state timber supply schedule.

Eleven simulations were made using DSL forest management costs, stumpage prices and discount, price and cost rates. Simulation results were compared to the base simulation run to determine elasticity of net operating income, management regimes and harvest volumes to changes in selected parameters.

The results substantiate that the models can be used to increase forest management efficiency. Managers can use the analytical methodology to improve strategic and operational decision-making.

-i i -

Acknowledgements

I would like to express my gratitude to my committee

members: Dr. Richard P. Withycombe, Dr. Teresa K. Beed and

Dr. Alan G. McQuillan. They took the time to provide

encouragement, criticism and advice during the course of

this study.

A special thanks goes to the Montana Department of State

Lands, Forestry Division; my employer. With the thoughtful

consideration and tolerance of Earl Salmonson, Forest

Management Bureau Chief, this study was possible to complete

on schedule. Also, my deep appreciation goes to the

employees of the Department who helped me so much: Pat

Flowers, Brian Long, Dave Remington and the many others.

Finally and most importantly, my humble appreciation and

love goes to my family, Debbie, Josh and Lara, who gave up

so much while I worked towards the Master of Business

Administration degree. Their unaltering faith in me, their

patience and their encouragement made it all possible and

wor thwhile.

-iii-

Table of Contents

Chapter 1 Introduction 1

1.1 Forestry Division, Department of State Lands. 3

1.2 The Computer Models. 5

1.2.1 PROGNOSIS. 6 1.2.2 SUPPLY and TIMLAN. 7

1.3 The Study Area. 10

Chapter 2 The Physical Data Base 11

2.1 Timberland Stratification 11 2.2 Inventoried Stand Records 14 2.3 Yield Regimes 18

Chapter 3 The Management Data Base 20

3.1 Regeneration Regimes 21 3.2 Financial Parameters 23

3.2.1 Project and Program Costs 23 3.2.2 Cost Trend 24 3.2.3 Stumpage Price 24 3.2.4 Price Trends 27 3.2.5 Discount Rate 29

Chapter 4 Study Results 32

4.1 Base Run Results 33 4.2 Price Sensitivity 35 4.3 Price and Cost Rate Stabilization

Sensitivity 42 4.4 Cost Rate Sensitivity 44 4.5 Price Rate Sensitivity 45 4.6 Discount Rate Sensitivity 47 4.7 Rotation Age Sensitivity 51 4.8 Management Regime Sensitivity 52 4.9 Regeneration Policy Sensitivity 55 4.10 TIMLAN Sensitivity 57

Chapter 5 Discussion and Conclusion 60

-i v-

5.1 Impressions of PROGNOSIS 62 5.2 Impressions of SUPPLY and TIMLAN 63 5.3 Selection of Stumpage Price Indexes 67 5.4 Strategic Forecasting and Decision Making

68 5.5 Budgeting Process 69 5.6 Conclusion 70

Appendix A Pertinent Assumptions in SUPPLY and TIMLAN Models 73

Appendix B SWLO Commercial Timberland Acres By Area and Habitat Group (In

Thousand Acres) 75

Appendix C Habitat Group Yield Response to Selected Yield Regimes 77

Appendix D Base Run Results From $10 to $100 per mbf

Stumpage Price Range 82

D.l Habitat Group 2 Results 83 D.2 Habitat Group 3 Results 85 D.3 Habitat Group 4 Results 87 D.4 Habitat Group 5 Results 89

Appendix E Base and Sensitivity Run Results At $50 per mbf Stumpage Price

91

E.l Run 1 Results 92 E.2 Run 2 Results 93 E.3 Run 3 Results 94 E.4 Run 4 Results 95 E.5 Run 5 Results 96 E.6 Run 6 Results 97 E.7 Run 7 Results 98 E.8 Run 8 Results 99 E.9 Run 9 Results 100 E.10 Run 10 Results 101

Appendix F Bibliography 102

- v-

List of Tables

Table Page

1 Stratified SWLO Timberland Acreage 13

2 Habitat Group and Stand Summary 16

3 Selected Stand Statistics 17

4 Regeneration Regimes for Area Class 2, Habitat Group 4 22

5 Average Timber Stand Improvement Costs (In $ per Ac.) 25

6 Weighted Lumber Price Index 27

7 Parameter and Result Summary for Base and Sensitivity Runs 34

8 Base Run Results at a $50 Stumpage Price 36

9 Price Sensitivity of Base Run Results, Habitat Group 4 39

10 TIMLAN Optimal Management Results 59

-vi-

List of Illustrations

Figure

1

2

3

4

5

6

7

8

Page

State Stumpage Prices from 1948 to 1983 in Historic and Constant (1972) Dollars. 26

Net Present Value, Present Cost and Annual Cut by Stumpage Price for Run 1, Area Class 1. 37

Net Present Value per Acre by Stumpage Prices for Run 1, Area Class 1. 42

Sensitivity of Region Harvest Volume and Net Present Value to Two Price and Cost Stabilization Times. 43

Sensitivity of Rotation Age to Two Price and Cost Rate Stabilization Times: A.C. 1, Habitat Group 4. 43

Sensitivity of Net Present Value and Harvest Volume to Two Cost Rates. 45

Sensitivity of Net Present Value and Harvest Volume to Selected Price Rates. 46

Sensitivity of Net Present Value and Annual Harvest Volume to Three Discount Rates.' 49

-vii-

Figure Page

9 Sensitivity of Net Present Value and Annual Harvest Volume to Two Rotation Age Objectives. 52

10 Sensitivity of Net Present Value and Annual Harvest Volume to Two Management Objectives. 53

11 Sensitivity of Marginal Acreage to Two Management Objectives. 54

12 Sensitivity of Net Present Value and Annual Harvest Volume to Two Management Objectives. 56

-v i i i-

Chapter 1

Introduction

From time to time, forest management concerns re-evaluate

their business environments, objectives, and strategic

plans. Most foresters today recognize that managed stands

can yield more wood than naturally developed and unmanaged

stands, and the long-term sustainable output of wood can be

quite different than that achieved under little or no

management. Before adopting intensive management practices,

four questions often raised by management personnel are:

(1) what combinations of forest management practices will

satisfy the organization's objectives, (2) given the optimal

combinations of forest management practices, what is the

sustainable timber harvest level, (3) will the added

investment of capital into more intensive forest management

practices increase the wealth of the organization and

(4) what effect do policy decisions have on wealth

maximization? This paper describes an analytical approach

to answering these questions.

-1-

Introduction -2-

The study is concerned with the assessment of maximum

timberland value derivable from perpetual timber production,

beginning with bare land. Two critical assumptions are

implicit in the above statement. First, it is assumed that

the most profitable use of timberland will remain timber

production. This is not to be construed to mean that

alternative land uses can't be compared to the results of

this study; they can by using the values of perpetual timber

production as a benchmark for judging other land uses.

Second, the price for standing timber (stumpage price) will

not change relative to timber production costs from one

rotation the next. These assumptions permit the use of

discounted cash flow analysis of a perpetual series,

1 otherwise known as the Faustmann solution (Faustmann 1849).

This study is limited to analyzing selected forest

investments under a clearcut scenario in which all

merchantable volume is removed at the time of final

harvest.

This study will not address harvest scheduling of

existing, standing timber, nor the forest management

1. McQuillan provides a detailed discussion of the theoretical and historical basis of economic analysis methodologies in forestry (1981, 12-52).

Introduction -3-

practices for standing timber which will maximize

organizational wealth. These issues, while extremely

important to the forest management organization, are beyond

the scope of this paper.

1.1 Forestry Division, Department of State Lands.

The study will occur within the Forestry Division of the

Montana Department of State Lands. The Montana Department of

State Lands (DSL) is charged with managing nearly 5 million

acres of state-owned lands to finance public schools and

institutions of higher education. This objective was first

mandated in the federal Organic Act of the Territory of

Montana (enacted 1864) and later in the federal Enabling Act

of 1889, in which the State of Montana was given federal

lands for managing and financing its education system. The

state constitution and various state statutes were enacted

following the Enabling Act which tended to redefine the

basic purpose of trust land management. Today, the primary

objective remains unchanged. However, certain constraints

have been added which relate to the maintenance of water

quality and the recognition of other social benefits to the

citizenry (Moon 1970, 2,9,32).

Introduction -4-

The Department of State Lands is divided into four

divisions, which are responsible for managing the trust

lands: administration, operations, lands, and forestry. The

Administration Division provides overall department

direction. The Operations Division is charged with

on-the-ground management of state land for all usas. The

I } Lands Division administers about I 4,421 thousand j acres

\r —-J suitable for grazing, agriculture ana other uses.

The Forestry Division of the Department of State Lands is

charged with administering the management of 579 thousand

acres of state-owned commercial timberlands to produce

sustained revenue for the state's educational system.

The Forestry Division is presently re-evaluating its

forest management program. Existing forest planning methods

are available to the division from which it can identify

harvest levels based upon existing yield tables developed

for unmanaged forest stands. These existing methods do not

identify economically optimal management regimes for timber

production and can significantly underestimate managed stand

yields. Using these methods may result in the same

criticism received by other agencies for not first

identifying economically justifiable management practices

(NFPA 1980; McQuillan 1981, 5,48-51).

Introduction -5-

This study will test certain computer models to determine

their usefulness for analyzing optimal management

intensities, production capacities and capital budgeting

justifications.

1.2 The Computer Models.

This pilot project tested three computer-run models to

identify economically optimal timber management regimes, to

determine a steady-state harvest schedule, and to identify

the effects of the management regimes on the present value

2 of the timber resource managed by the division. Should the

models perform satisfactorily, they may be utilized in the

division's study of its timberland management program.

2. A timber management regime is a combination of different forestry activities. For the purposes of this study, a management regime will be composed of two component regimes: the selected method of regeneration and the selected combination of treatments that, once the stand is established, influence the stand yield. Regeneration regimes can be composed of planting, seeding or natural regeneration by clearcutting, shelterwood cuts or other regeneration harvest method. Yield regimes can be combinations of precommercial thinnings, commercial thinnings and initial shelterwood cuts (McQuillan 1981, 55-56) .

Introduction -6-

The models used in this project were PROGNOSIS, SUPPLY

and TIMLAN. Each model will be briefly described below.

1.2.1 PROGNOSIS.

PROGNOSIS is a computer program designed to simulate the

growth and yield of forest stands. It was created by Dr.

Albert R. Stage, project leader and principal

mensurationist , and his colleagues at the Forest Sciences

Laboratory, Moscow, Idaho. The Inland Empire version 4.1 was

used in this project.

The program is an individual tree, distance independent

model which projects forest stand development over time. It

offers considerable flexibility in simulating a wide variety

of forest management practices on a broad range of timber

stands in the Inland Empire region.

The model incorporates the average effects of all factors

that influence forest development, such as disease, insects,

silvicultural treatments and climatic influences. This is

accomplished to the degree that the data in which the model

was fitted reflected these factors. The data base was quite

large, consisting of stand inventories from the Region One,

U.S. Forest Service inventory program and forest research

inventories (Wykoff, Crookston, and Stage 1982, 1-2).

Introduction -7-

PROGNOSIS is widely available and used by many forest

management organizations. In this study, the model was used

to develop Scribner board foot yield tables for each forest

management regime.

1.2.2 SUPPLY and TIMLAN.

SUPPLY and TIMLAN are computer models which evaluate the

selection of timber management regimes and rotation ages as

they affect net present value of a perpetual series of

timber crops. The models evaluate bare land on a site

specific basis. They were developed by Dr. Alan G.

McQuillan at the University of Montana. TIMLAN is embedded

in the SUPPLY computer program. The assumptions intrinsic

to the two models are described in Appendix A.

Each of the two models analyze the capital budgeting

problem in slightly different ways. SUPPLY evaluates

management regimes and rotation opportunities for an

exogenously determined range of regional stumpage prices and

produces a steady-state timber supply schedule.

SUPPLY uses an embedded multiple regression equation to

estimate an average lumber price index using embedded

coefficients and parameters developed from Lolo National

Forest data. After determining an average lumber price, the

Introduction -8-

model calculates specific stumpage prices for each area

class and habitat group, based upon median diameters of

harvested trees, whether harvesting was by cable or tractor

systems, whether clearcutting or shelterwood silvicultural

systems were used, and an average lumber price index.

(Jackson and McQuillan 1979).

SUPPLY answers the question:

"Given a large range of possible management regimes and rotation ages, predictable costs and known probabilities of regeneration success, what regimes and rotations would maximize net present... value under a specified range of possible output (stumpage) prices?" (McQuillan 1981, 54).

TIMLAN uses a single estimation of stumpage price rather

than a user-specified range of prices, as in SUPPLY.

Stumpage price is estimated by the same harvest

characteristics described for SUPPLY, except that the user

supplied rates of price and cost change are applied to the

lumber price index and conversion (manufacturing) costs.

TIMLAN escalates lumber price and conversion costs and

determines stumpage price as a residual value. The

algorithm was developed by Merzenich (1979) and incorporated

into TIMLAN by McQuillan. This method avoids the potential

problem of projecting stumpage prices at a rate which

surpasses the forecasted prices for lumber at some future

time; a condition which has been occurring since the end of

Introduction -9-

World War II (U.S.D.A. Forest Service 1973).

TIMLAN answers a slightly different question:

"Given a range of management regimes and rotation ages, known costs and lumber price indices, what is the net present value of each alternative and which will maximize NPV?" (McQuillan 1981, 55).

TIMLAN is well suited for investment analysis since it

models the analyst's perception of stumpage prices, costs

and trends for a range of alternatives.

The SUPPLY program processes data in a series of nested

loops. It first initializes constants and reads the run

specific input data. It progresses through the following

series of loops (McQuillan 1981, 60):

1. Read area class specific data for one area class.

2. Read habitat group specific data and all yield data for one habitat group.

3. Read regeneration regime data for one regime.

4. Loop through one yield regime at a time.

5. Loop through one regional stumpage price at a time (for SUPPLY). TIMLAN uses one stumpage price estimate.

6. Compute optimal rotation age and associated present cost and revenue data for management regime. Save if better result.

7. Recycle by price (step 5).

8. Recycle by yield regime (step 4).

9. Recycle by regeneration regime (step 3).

Introduction -10-

10. Save data for optimal management regimes which best meet objective criteria in habitat group and area class for each price level.

11. Recycle by habitat group (step 2).

12. Print results for all habitat groups in area class, accumulate totals and recycle by area class (step 1).

13. Print summary for entire region

14. Stop.

1.3 The Study Area.

The study used the Southwestern Land Office of DSL as the

test area. According to DSL records, the Southwestern Land

Office (SWLO) manages over 215 thousand acres of state land,

of which 128,771 acres are considered commercial

timberland. The seven counties included in the SWLO area

are Deer Lodge, Granite, Mineral, Missoula, Powell, Ravalli

and Silverbow (Montana Dept. of State Lands 1982).

Chapter 2

The Physical Data Base

Two data bases are required for the computer programs:

the physical and management data bases. The management data

base is the subject of the next chapter. The physical data

base is comprised of yield tables which represent growth and

yield responses to different timber management regimes.

2.1 Timberland Stratification

The forests in Montana are particularly diverse in their

composition, structure, productive capacities and

regenerative characteristics. These differences are, to a

large degree, reflective of physical and climatic conditions

which have occurred on specific sites. In addition to

influencing stand characteristics, these environmental

differences influence land use and values. For these

-11-

The Physical Data Base -12-

reasons, the SWLO commercial timberland acreage was

stratified into groups reflecting these differences using

the state-wide forest resource inventory data and

silvicultural input by the SWLO timber stand improvement

forester (DSL, Forestry Division 1982, Long 1984, Remington

1984).

The SWLO commercial timberland acreage was stratified

into two primary land uses which are consistent with

organizational objectives: land influencing water quality

and land available for timber production without

restrictions. This study will focus on available timber

production acres.

The available timberland acreage was stratified into area

classes which represent acres with common slope

characteristics, availability of natural seed sources, and

productivity groups (table 1). The criteria used to stratify

the acreage were selected because of their influence on

logging and slash disposal methods, regeneration methods,

potential yields and costs.

Habitat type classifications for Montana forests were

used as the basis for measuring stand productivities

(Pfister et al. 1977). The habitat types were aggregated

into seven groups on the basis of similar stand

productivities and probabilities of natural regeneration


success by Mr. Dave Remington, TSI Forester for the SWLO

(Appendix B). Two habitat groups were combined with other

similarly productive groups to obtain sufficient stand data

(group 1 was combined with group 2 and group 6 with group

5).

Table 1

Stratified SWLO Timberland Acreage

AREA CLASS NO.

SLOPE CLASS (%)

SEED SOURCE AVAIL.

HABITAT GROUP NO.

AVAIL. TIMBER ACRES

TOTAL ACRES

1 <40 YES 1,2 3 4

5,6

4, 762 4,321

36,030 1 , 730 46,843

2 <40 NO 1,2 3 4

5,6

4,333 2,880

19,401 1,730 28,344

3 >40 YES 1,2 3 4

5,6

1,190 2,881

15,441 4,036 23,548

4 >40 NO 1,2 1,084 3 1,920 4 8,315

5,6 4,036 15,355

TOTAL AVAILABLE TIMBERLAND ACRES: 114,090

Source: Remington, D. 1984. SWLO TSI Forester. Personal interview with author.


A submarginal site (group 7) was not included in this

analysis due to its very low productivity and numerous

3 management limitations.

2.2 Inventoried Stand Records

Stand records used in this project were available from

DSL's state-wide forest inventory, which was completed in

1978 for the SWLO area (Montana Dept. of State Lands 1982).

The inventory is scheduled to be updated every ten years.

Fourteen seedling and sapling plot records were selected

by the author to represent the stratified SWLO timber

acreage (table 2). A total of 598 inventoried stands in the

SWLO area were manually screened to select stands most

reflective of managed stand conditions. Selection criteria

included: (1) young stands, preferably ten to twenty years

old but less than fifty, (2) major basal area component was

in the sapling size class, (3) stands from state land were

preferred, but stands from other owner groups were

acceptable, and (4) measured tree characteristics did not

indicate past suppression.

3. Habitat types are ecological classifications based upon forest climax vegetation.


The selected stand records were modified by the author to

reflect stand conditions following clearcutting (table 3).

The modifications were of two types. First, individual tree

records which obviously were not a component of the

seedling/sapling stand structure were deleted, since they

would not be a stand component after clearcutting.

Secondly, seedling records which were too small to obtain

height measurements were deleted because of the confounding

influence of ingrowth in developing yield tables. These

changes were accomplished by reviewing tree age, diameter

and height measurements.


Table 2

Habitat Group and Stand Summary

MEAN HABITAT GROUP STEMS SWLO

N O . OF PER AC. AVAIL. GENERAL SELECTED BEFORE TIMBER

NO. CHARACTERISTICS STANDS PCT [a] ACRES [b]

1,2 Highly Productive >85 cuft/ac/yr

Warm, moist sites

Medium Productive 50-85 cuft/ac/yr

Cool, moist sites

Medium Productive 50-85 cuft/ac/yr Warm, dry sites

Low Productive 20-49 cuft/ac/yr Hot, dry sites

4 926 11,369

3 1,034 12,002

5 614 79,187

2 254 11,532

a.

b.

PCT means precommercial thinning.

Data derived from SWLO inventory data.


Table 3

Selected Stand Statistics

HT.GR. STAND TREES MEAN STD. NO. NO. PER ACRE AGE DEV.

1,2 SW037 517 39.6 7.9 SW114 1615 41.6 9.6 SW358 984 25.2 5.3 SW330 588 39.2 8.0

AVG. 926 36.4 7.7

3 SW017 958 30.2 3.8 SW020 1050 9.9 2.1 SW164 1095 40.9 7.1

AVG. 1034 27.0 4.3

4 SW064 550 27.0 10.2 SW028 917 27.7 5.3 SW025 747 21.9 3.9 SW087 399 38.0 7.5 SW041 459 39.2 11.1

AVG. 614 30.8 7.6

5,6 SW051 125 43.9 7.8 SW342 382 18.0 1.7

AVG. 254 30.9 4.7

Note: Data derived from 1978 state-wide inventory.


2.3 Yield Regimes

Stand development was simulated for six yield regimes

using PROGNOSIS and an average determined for each

regime/habitat group combination. The yield regimes

included a control (do nothing) and 5 combinations of

4 precommercial thinning and commercial thinnings. The

regimes are indicative of a partial list of realistic

scenarios which the forester may consider. The yield

regimes are listed below.

1. Do nothing. Let the stand grow naturally.

2. Precommercial thin from below (removing the smallest trees first) to 300 trees per acre at age 15 in habitat groups 1, 2 and 3, at age 20 in group 4 and at age 25 in groups 5 and 6.

3. Precommercial thin as stated above and commercial thin from below to 60 square feet of stand basal area at age 70.

4. Precommercial thinning is the removal of excess, noncommercial trees from a young stand of trees with the objective of concentrating future growth and volume onto crop trees. Commercial thinning is the removal of merchantable, pole-sized trees (5-11 inch diameter trees) or small saw log trees (12-24 inch diameter trees) with the objective of obtaining an intermediate cash flow to the land owner and to concentrate future growth and volume onto residual, crop trees.


Precommercial thin as stated in (2) and commercial thin from below to 60 square feet of stand basal area at age 80.

Precommercial thin as stated in (2) and commercial thin from above to 60 square feet of stand basal area at age 70.

Precommercial thin as stated in (2) and commercial thin from above to 60 square feet of stand basal area

5 at age 80.

The yield tables averaged from the PROGNOSIS simulations

represent the physical data base which was input into

SUPPLY. Graphical representation of the yield tables by

6 habitat group is displayed in Appendix C.

A potential deficiency in the yield results may be that

natural stand development (regime 1) overestimated yields.

This concern is based on field observations where total

trees per acre in natural stands are higher than those

simulated. Higher than normal stand densities should lower

stand growth and yields due to excessive tree competition.

5. Basal area is a stand density measure used in determining growing stock levels. It is the surface area (in square feet) of tree stems on an acre, measured 4.5 feet above the ground.

6. Most likely yield regimes were simulated. The results and interpretations of the economic analysis are limited only to the regimes and stand conditions which were simulated.

Chapter 3

The Management Data Base

As mentioned in Chapter 2, two data bases are required

for SUPPLY; the physical and management data bases. This

chapter will discuss the management data base.

The management data base is composed of user-supplied

variables which included estimates of financial and

regeneration regime parameters. The financial parameters

included a selection of management objectives and estimates

of costs, prices, trends and tax rates. The regeneration

regime parameters included estimates of regeneration

alternatives, success probabilities and regeneration time

delays. Because the regeneration regime parameters are

strongly influenced by management and subject to

considerable subjectivity, the parameters are considered

management related and are included in this chapter.

-20-

The Management Data Base -21-

3.1 Regeneration Regimes

Ten regeneration regimes were selected for simulation in

SUPPLY. The regimes represent activities most likely to be

used in the Southwestern Land Office (SWLO) area.

Each regeneration regime consisted of three activities.

The activities consisted of a choice of site preparation, a

choice of initial regeneration and a choice of subsequent

regeneration, should the first attempt fail.

Three site preparation methods were analyzed: no site

preparation, tractor scarification on gentle slopes angled

40 percent or less and broadcast burning on steep slopes

greater than 40 percent.

Initial and subsequent regeneration methods were natural

7 regeneration, planting and interplanting. Parameters were

entered into SUPPLY to account for expected time delays and

success probabilities for each regeneration method. These

7. Interplanting is a method of artificial reforestation where the seedlings are planted into a partially regenerated site. Planted seedlings per acre and planting cost per acre are usually less for inter planting than planting.


parameters were varied for each of four area classes and

four habitat groups (see table 4).

Table 4

Regeneration Regimes for Area Class 2, Habitat Group 4

SITE -INITIAL SUBSEQUENT PREP REGEN TIME PERCENT TIME PERCENT

REGIME MODE MODE DELAY PR0B. REGEN DELAY PR0B. NO. [a] [b] ( YRS) SUCCESS MODE (YRS) SUCCESS

1 None Nat. 20 80 Nat. 20 70 2 None Nat. 20 80 Plant 6 40 3 None Nat. 20 80 Inter 6 40 4 None Plant 5 40 Nat. 20 70 5 None Plant 5 40 Plant 6 40 6 None Plant 5 40 Inter 6 40 7 Scar Nat. 20 80 Nat. 20 70 8 Scar Nat. 20 80 Plant 4 40 9 Scar Plant 5 60 Nat. 20 70 10 Scar Plant 5 60 Plant 4 40

Note: Data derived from information provided by Remington during personal interview in 1984.

a. None means no site preparation. Scar means tractor scarification. In area classes 3 and 4 (slopes > 40%), broadcast burning was substituted for tractor scarification.

b. Nat. means natural regeneration. Plant means planting. Inter means inter planting.


3.2 Financial Parameters

SUPPLY offers considerable flexibility for entering

specific costs, prices, trends and federal and state tax

parameters. This study is concerned with a state government

agency, and the tax consequences of capital budgeting were

not analyzed. The other parameters are discussed in the

following subsections.

3.2.1 Project and Program Costs

Historical cost data were not available for the study.

Much of the past timber stand improvement work had been

completed by Comprehensive Employment and Training Act and

other work-experience training programs. The production

rates and costs from subsidized projects were not indicative

of production rates and costs usually experienced by

contractors, which the DSL currently uses. Therefore, only

1983 contract rates were used to estimate per acre costs for

planting, precommercial thinning and site preparation.

An accounting change in 1981 precluded the use of prior

forest management program costs. The actual costs in 1982

were abnormally high and were not used. The actual timber


sale preparation and administration costs reported for

fiscal year 1983 were converted to costs per thousand board

feet (mbf) harvested and treated as variable expenses, since

they tend to be constant per unit of output. Overhead costs

for 1983 were converted to cost per acre and treated as a

fixed cost, since they tend to 4 be independent of volume

output (Gray and Johnston 1977, 119-125).

To meet SUPPLY data input requirements, the average rates

were deflated to 1972 constant dollars by the Gross National

Product implicit price deflator (table 5).

3.2.2 Cost Trend

Costs were assumed to increase at an annual real rate of

1.1 percent and stabilize after 50 years for the base

analysis.

3.2.3 Stumpage Price

SUPPLY reports net present values, mean annual volume

increments, rotation ages and optimal management regimes for

a user-specified range of stumpage prices. For this study,

a $10 to $100 per mbf range was specified. The stumpage

prices are in 1972 constant dollars. The equivalent 1983

stumpage price range is about $21 to $214 per mbf. This


range provides a safe margin for the analyst, based on

historic state stumpage prices (figure 1).

Table 5

Average Timber Stand Improvement Costs (In $ per Ac.)

1983 1972 ACTIVITY AVG. COST COST [a]

Broadcast Burning [b] Tractor Scarification [c] Planting 600 TPA [d] Interplanting 300 TPA Precommercial Thinning:

Slopes <40% Slopes >40%

Presale and Sale Admin. Overhead

34.00/ac. 30.00/ac.

147.50/ac. 88.50/ac.

92.50/ac. 120.00/ac.

15.59/mbf[e] 0.68/ac.

15.00/ac. 14.00/ac. 69.00/ac. 41.00/ac.

43.00/ac. 56.00/ac. 7.28/mbf 0.32/ac.

a. Estimated 1983 GNP implicit price deflator = 2.1407.

b. Broadcast burning costs were assumed to be 60% for brush disposal (a cost of logging and, therefore, a sunk cost) and 40% for scarification (a relevant investment cost).

c. Tractor scarification costs were assumed to be 60% for brush disposal and 40% for scarification.

d. Planting cost includes nursery and hand planting costs. TPA means trees per acre.

e. Mbf means thousand board feet, Scribner log rule.

The TIMLAN model uses a single estimated stumpage price

to compute the best management regime and rotation age. The


estimated stumpage price is not directly input by the user.

Rather, the user inputs a weighted average lumber price

index (in 1972 dollars) for each habitat group along with an

expected cost and price trend. An algorithm calculates

stumpage price index and conversion cost (Merzenich 1979,

36-45).

STftTE STOMPAGE PRICES 100 •

9 0 -

Mi

80 • 70

M «.a 6Q

V A. 50 -

a u

48 • 30

M A A

10 0

/V

/ / v

f J JA.V,\ >

I 1 1 \ I 1~I 1 1 » 1 I I I I I ^ I 1 t » i i 1 I J * I n k I I I I 1 I 4 5 5 5 5 5 6 6 6 6 6 7 7 7 7 7 88 8 0 2 4 6 8 O 2 4 6 8 0 2 4 6 8 0 2

FISCAL VEftRS Historic S —1972 $

Figure 1. State stumpage prices from 1948 to 1983 in historic and constant (1972) dollars.

This study used the 1983 Western Wood Products

Association lumber price index (WWPA index) to derive a

species weighted average index (table 6). Board foot volume

at age 80 was the basis for determining the species

weights. An index was calculated for each stand record,


averaged within habitat groups and deflated to constant 1972

dollars using the GNP implicit price deflator.

Table 6

Weighted Lumber Price Index

1983 PERCENT SPECIES MIX [b] WWPA

INDEX HABITAT GROUP NUMBERS SPECIES [a] 1,2 3 4 5,6

Douglas-fir 215 30 4 59 50 Western Larch 215 29 6 0 0 Ponderosa Pine 312 0 29 41 50 Lodgepole Pine 206 22 61 0 0 Other White Woods 206 21 0 0 0

AVG. WWPA INDEX (1983 $ ) : 212 238 254 264

AVG. WWPA INDEX (1972 $ ) : 0.99 1.11 1.19 1.23

a. WWPA Index means Western Wood Products Association lumber price index. b. Percentages may not sum to 100 due to rounding.

3.2.4 Price Trends

Price trend was analyzed using stumpage price time-series

data from 1948 through 1983. Stumpage prices were

logarithmically regressed against years to determine the

historic growth rate (Makridakis and Wheelwright 1978,

200-201). A 3.3 percent growth rate was determined for the


2 deflated stumpage prices (r =0.63). This rate was also used

in TIMLAN to project lumber prices.

These results compare conservatively to U.S.D.A. Forest

Service data. In deflated values (1967 dollars), the Rocky

Mountain region has experienced a 3.70 percent trend in

stumpage prices from 1952 through 1976. The Forest Service

projects a 3.81 percent trend from 1976 through 2030 with a

cyclical increase of 9.1 percent from 1976 to 1990 in

response to a projected surge in residential home building.

The rates of increase decline during the four decades

following 1990 (U.S.D.A. Forest Service 1982, 204-205).

The economic recession of the early 1980s, when housing

starts plummeted to about 1 million from over 2 million in

1978, will likely dampen the 9.1 percent and 3.8 percent

projections by the Forest Service (Anderson and Schaertl

1983). Despite the latent demand for new housing that may be

an impetus for strong growth in the forest products

industry, housing starts should continue to be depressed by

mortgage rates in excess of 12 percent. Indeed, interest

rates are expected to continue to increase in response to

Federal Reserve Board and federal administration policies to

combat record high federal deficits and inflation (Blackman

1984).


Based on the historic increase of state stumpage prices

and the economic uncertainty surrounding the forest products

industry, the base analysis included a 3.3 percent real rate

of increase in stumpage prices. The trend was assumed to

stabilize in 50 years to assure that projected stumpage

prices do not increase above projected lumber prices.

3.2.5 Discount Rate

The discount rate is the cost of capital at which future

cash flows will be discounted. This rate accounts for the

time value of money; the uncertainty of receiving future

cash for money spent today, the opportunity associated with

using cash today instead of investing it for future return

and the effect of inflation and deflation on cash. Using

discounted cash flow analysis for selecting forest

investments is usually sensitive to the discount rate used.

Because of the sensitivity normally associated with

discount rates, DSL critically analyzed its business

environment and selected a 3.3 percent rate based upon the

present portfolio mix of invested securities by the state

and the risk associated with stumpage price variability.

Historically, the state's investments have a slightly

negative return, excluding inflationary effects. Currently,


about 60 percent of the school trust funds is invested in

government bonds, while the remaining 40 percent is invested

in corporate bonds. Using this investment structure and

evaluating the yield of bonds since 1960, the annual return

would have been 2.70 percent. Variability associated with

stumpage revenues added another 0.54 percent for risk.

Combining the two percentages results in the proposed 3.3

percent discount rate for DSL forest investments (Montana

Dept. of State Lands 1984). The 3.3 percent discount rate

was used in the base analysis.

The discount rate does not cover the risks associated

with growing timber for long periods of time. Accounting

for this risk can be handled in two separate ways. First,

an added risk premium can be added to the discount rate.

Second, the timber yields can be adjusted to reflect the

probability of catastrophic loss.

Catastrophic losses can be quite real over a rotation

period on a given acre of land. Nevertheless, the overall

effect on a forest region is small. This may be

particularly true in future forests where roads and other

improvements are in-place, permitting quick response of

equipment and manpower. Another confounding issue of


accounting for catastrophic losses is that the salvage value

of the damaged timber increases with tree size but decreases

with increasing intensity of the event.

An adjustment for the probability of catastrophic loss

was made in this study. PROGNOSIS reports Scribner board

foot volume based on an eight inch tree top diameter, one

foot stump height and a nine inch minimum bole diameter

(Wykoff, Crookston, and Stage 1982, 31). Most timberland

owners require more stringent merchantability standards than

those in the model. If the more stringent standards were

used, per acre yields would be larger. The risk of

catastrophic losses was assumed to equal the differential

between the two merchantability standards.

Chapter 4

Study Results

The two preceeding chapters discussed the development of

the physical and management data bases. In this chapter,

the results of the base run and sensitivity analyses will be

discussed. Before beginning a discussion of the results, it

may be beneficial to summarize the key parameters used in

the base and sensitivity analyses.

The study includes four habitat groups for each of four

area classes identified for the Southwestern Land Office

area (refer to table 1 on page 13). Two habitat groups were

combined into adjacent groups to obtain sufficient

acreages. The combined habitat groups 1 and 2 will be

referenced as habitat group 2, since group 2 represents more

acreage. Combined groups 5 and 6 will be referenced as

group 5, since group 5 also represents more acreage.

The management data base consisted of stand tables which

-32-

Study Results -33-

list the average board foot yields (in Scribner rule) and

the median diameters for stand ages 10 to 140 years by

decade. The stand tables also list the volume and median

diameter of prescribed removals. The stand tables were

developed for each combination of six yield regimes and four

habitat groups (refer to page 18 and Appendix C).

The financial data base consisted of ten regeneration

regimes (see table 4 on page 22) and specific financial

parameters. The financial parameters established program

and project costs (see table 5 on page 25), a range of

stumpage prices from $10 to $100 per mbf (in 1972 dollars)

for the SUPPLY model and weighted average lumber price

indexes for the TIMLAN model (refer to table 6 on page 27).

Specific financial parameters for the base and sensitivity

runs are shown in table 7, along with selected regional

results. All dollars will be in constant 1972 dollars,

unless specifically stated otherwise.

4.1 Base Run Results

The base run reflected the most likely set of parameters

for the SWLO. The results will be discussed for a range of

stumpage prices (in 1972 dollars) from $10 to $100 per

Study Results -34-

thousand board feet (mbf). The regressed, deflated stumpage

prices estimated a 1983 price of approximately $46 per mbf.

The discussion will center on the $50 per mbf results as

being most consistent with SWLO conditions.

Table 7

Parameter and Result Summary for Base and Sensitivity Runs

REGION RESULTS AT 150/MBFIa] RUN DISC PRICE COST YRS. TO HBMT ROT. REFOR. NPV Eb] PC Ec3 ANN. CUT NO. RATE RATE RATE STABAL. OBJ. OBJ. POLICY PROGRAM (MM) (MM$) (MBF)

> Base Run <

1 3.3 3.3 1.0 50 Max.NPV Max.NPV None SUPPLY 3,871 838 12,226

> Sensitivity Runs Edl <

2 10 798 348 11,819 3 0.0 4,377 1,033 13,495 4 3.8 5,229 1,422 13,362 5 2.6 2,527 772 12,172 6 4.0 1,399 456 11,674 7 5.0 357 138 9,191 8 CMAI-BF [el 2,068 1,415 21,846 9 Max.MAI CMAI-BF Ef3 (984! 4,681 23,858 10 <=10 yrs. 3,272 1,757 12,855 11 TIMLAN 10,456 2,251 14,813

a. In 1972 dollars. $50/abf stuipage price does not apply to TIMLAN results. b. NPV leans net present value. c. PC leans present cost of discounted cash outflows. d. The changed parameters are entered below. No entry in a row leans that no change occurred froi run 1. e. CMAI-BF leans culiination of lean annual increment in board feet. In this run, the rotation age objective was applied only to lanageient regiies which had financial values of 0 or greater. f. Max.MAI leans aaxiiize lean annual increient and is analogous to footnote Ce.l except that no financial objective was applied first.

Study Results -35-

The SWLO region is capable of contributing over $3.8

million in wealth (net present value) and sustain an annual

harvest of 12.2 million board feet of timber from its

available timberland, based on a $50 per mbf stumpage price

(table 7).

Regeneration regimes were predominately low intensity at

$50 per mbf (table 8). Natural regeneration was the optimal

initial regeneration method on all habitat groups. Site

preparation and subsequent artificial regeneration attempts

(either planting or interplanting) were optimal in area

class 1 and in habitat group 2 of all area classes. No site

preparation and subsequent natural regeneration attempts

were preferable in the remaning habitat groups.

No thinning (do nothing regime) was optimal for all

habitat groups and area classes at a stumpage price of $50

per mbf.

4.2 Price Sensitivity

As stumpage prices rose, management intensity and net

present value increased, but rotation ages generally

declined. Under high stumpage prices ($90 to $100

Study Results -36-

Table 8

Base Run Results at a $50 Stumpage Price

HB REGEN YLD ROT HAR NPV MAI MED GR REG. REG AGE AGE /AC /AC DIA NO NO. NO. (Yr) (Yr) ( $ ) (bf) (In)

[a] [b] [c] [d] [e] [f] [g] [h]

Area Class 1 (46, 843 ac. )

2 7 1 55 50 86. 05 126. 22 12 .3 3 7 1 87 80 36. 11 128. 24 11 .0 4 7 1 87 80 33. 57 106. 53 11 .9 5 8 1 75 60 56. 00 127. 36 13 .2


2 8 1 58 50 69. 73 119. 69 12 .3 3 1 1 97 80 28. 06 115. 02 11 .0 4 1 1 97 80 26. 25 95. 55 11 .9 5 3 1 85 60 44. 27 112. 38 13 .2


2 7 1 56 50 58. 87 123. 96 12 .3 3 1 1 93 80 22. 71 119. 97 11 .0 4 1 1 93 80 22. 73 99. 66 11 .9 5 3 1 79 60 49. 23 120. 91 13 .2

Area Class 4 (15, 355 ac. ) '

2 7 1 60 50 49. 39 115. 70 12 .3 3 1 1 97 80 19. 42 115. 02 11 .0 4 1 1 97 80 19. 08 95. 55 11 .9 5 3 1 85 60 33. 12 112. 38 13 .2

a. HB GR NO means habitat group number. b. REGEN REG. NO. means regeneration regime number. c. YLD REG. NO. means yield regime number. d. ROT AGE means rotation age. e. HAR AGE means harvest age. f. NPV/AC means net present value per acre. g. MAI/AC means mean annual increment per acre. h. MED DIA means median stand diameter.

Study Results -37-

per mbf), intensive yield regimes consisting of commercial

thinnings resulted in longer rotation ages. This

demonstrates the substitutability of management intensity

and time in financial analyses.

Present costs (PC) and net present value (NPV) increased,

as expected, with the difference between NPV and PC

increasing substantially over the stumpage price range of

$10 to $100 per mbf (figure 2).

V\ 'N e 3

15.0

12.5

1@. 0

7.5

5 . 0

2.5-1

@ . 0

REGION RESULTS - RUN 1

fi J

jifrl

10 20 30 40 50 60 70 80 90 "0q

S tiiMpasfe Price C1972 $/Mhf) COST (mh$) • NPU ( 5 iUOL.(RMhf)

Figure 2. Net Present Value, Present Cost and Annual Cut by Stumpage Price for Run 1, Area Class 1.

Study Results -38-

Annual harvest volume, however, did not increase

consistently. Annual harvest decreased from 14.3 million

board feet to 12.2 million board feet as price increased

from $10 per mbf to $50 per mbf. As stumpage prices

increased from $50 per mbf to $100 per mbf, harvest volumes

increased to 14.2 million board feet.

The erratic behavior of annual harvest is responding to

increasing stumpage prices with little corresponding

increase in management intensity and cost. Holding

management intensity constant and increasing the price

resulted in shorter rotation ages. When management

intensity and variable costs optimally increased, a

corresponding increase in harvest volume occurred. That is,

investing in more intensive management resulted in faster

stand growth and larger stand yields which could be

harvested.

Optimal management regimes for habitat group 4 favored

initial natural regeneration and no thinning over the range

8 of stumpage prices (table 9). Rotation ages ranged from a

8. Habitat group 4 contributed the greatest acreage to each area class. Therefore, it will be discussed. Appendix D contains results for the four habitat groups by area class.

Study Results -39-

low of 87 years to a high of 107 years with an average of

about 90 years predominating. Simulated stand age at

harvest was 80 years in most all cases.

Table 9

Price Sensitivity of Base Run Results, Habitat Group 4

STUMP REGEN YLD ROT HAR NPV MAI PRICE REG. REG AGE AGE /AC /AC

($/mbf) NO. NO. (Yr) (Yr) ($) (bf)

Area Class 1 (36,030 ac.)

10 1 1 103 90 11.62 118 20 1 1 93 80 16.27 100 30 7 1 87 80 21.88 107 40 7 1 87 80 27.72 107 50 7 1 87 80 33.57 107 60 7 1 87 80 39.41 107 70 7 2 87 80 45.78 119 80 7 2 87 80 52.32 119 90 7 2 87 80 58.85 119 100 7 5 97 90 66.32 133

Class 2 (19, 401 ac. )

10 1 1 97 90 9.77 114 20 1 1 97 80 13.80 96 30 1 1 97 80 17.95 96 40 1 1 97 80 22.10 96 50 1 1 97 80 26.25 96 60 1 1 97 80 30.41 96 70 3 2 95 80 35.22 109 80 3 2 95 80 40.19 109 90 3 5 105 90 45.25 123 100 3 5 105 90 51.03 123

Table continued on next page.

Study Results -40-

Table 9 Continued


($/mbf) NO. NO. (Yr) (Yr) ( $ ) (bf)

Area Class 3 (15 ,441 ac. )

10 1 1 103 90 3.93 118 20 1 1 103 90 8.39 118 30 1 1 103 90 12.84 118 40 1 1 93 80 17.56 100 50 1 1 93 80 22.32 100 60 7 1 88 80 27.73 105 70 7 1 88 80 33.38 105 80 7 1 88 80 39.03 105 90 7 1 88 80 44.68 105 100 7 1 88 80 50.32 105


10 1 1 107 90 3.04 114 20 1 1 107 90 6.94 114 30 1 1 107 90 10.83 114 40 1 1 97 80 14.93 96 50 1 1 97 80 19.08 96 60 1 1 97 80 23.23 96 70 1 1 97 80 27.39 96 80 3 1 95 80 31.59 98 90 3 1 95 80 36.03 98 100 3 1 95 80 40.48 98

Note: Column titles are defined in table 8 on page 36.

The difference between rotation age and harvest age is

the period of regeneration delay. The delay period usually

was around 13 years, ranging from 8 years to 17 years.

Study Results -41-

All acreage in the habitat groups were supra-marginal.

Net present value per acre increased as stumpage prices

increased (figure 3). Lower productive habitat groups

produced lower NPV per acre than adjacent, more productive

groups with the exception of habitat group 5. This anomaly

resulted from the abnormally high volume yields and diameter

growth of the projected stands in habitat group 5.

The relative difference between more productive and lower

productive groups increased as stumpage price increased.

That is, more productive lands became more valuable, while

less productive lands became relatively less valuable.

Mean annual increment decreased with increasing stumpage

prices until a more intensive management regime became

optimal. When management intensity increased, an increase

in mean annual increment occurred.

These results are consistent with the classical theory of

land rent.

"As the use of land increases, landlords receive higher payments from two sources:

1. Increased demand leads the community to employ land previously not good enough to use; the advantage of previously used land over the new marginal land increases, and rents go up correspondingly.

Study Results -42-

2. Land is used more intensively; the marginal revenue product of land rises, thus increasing the ability of the producer who uses the land to pay rent." (Baumol and Blinder 1982, 603).

NET PRESENT UALUE / ACRE

Q €

&

&

5 P* Z

150

125

190

75

50 -I

25

0

Jl 1 0

2 O

3 e

4 0

7 0

8 0

9 0

Stimpacre Price <1972 .GR.2 DHT.GR.3 HHT.GR.4 . GR. 5

1 0 0

Figure 3. Net Present Value Per Acre by Stumpage Prices for Run 1, Area Class 1.

4.3 Price and Cost Rate Stabilization Sensitivity

Run 2 tested the sensitivity of time before price and

cost trends stabilized. Rates were assumed to increase for

ten years in run 2, whereas, rates increased fifty years in

run 1. NPV and annual harvest volume decreased in run 2, but

rotation age generally increased (figures 4 and 5).

Study Results -43-

PSC RATE STAB. SENSITIUITV

W u

v

& 5l <£

C 0

G B

S tuwpasfG Price <1972 $/n3if) H3 50 Vears • 10 Vears

Figure 4. Sensitivity of Region Harvest Volume and Net Present Value to Two Price and Cost Stabilization Times

W

15. 0

12,5

10. 0

7,5

5 . 0

2 . 5

O. 0

P/"C HATE STAB. SENSITIUITU

Annual Cut

< G4Mi$ >

=U _pED I i gi

2 0

3 8

4 O

5 6 Q

7 ©

8 O

S tiuipage Price <$/>*£>£} U 10 years -S 5© 'iears

9 1 & &

Figure 5. Sensitivity of Rotation Age to Two Price and Cost Rate Stabilization Times; A.C. 1, Habitat Group 4

Study Results -44-

A ten year stabilization period only slightly affected

area class results. Since the differential between price

and costs rates had less time to influence cash flows, the

results favored no site preparation, longer rotation ages,

and lower mean annual increments. Harvest ages did not

change from base run results. Net present values per acre

decreased to about 20 percent of base run results (Appendix

E. 2) .

4.4 Cost Rate Sensitivity

Costs were not allowed to increase each year in run 3. In

the base run, costs increased at 1.0 percent per year.

Compared to the base run results, the net present value

(NPV) increased an average of 14 percent and annual harvest

volume increased 6 percent as stumpage price ranged from $10

to $100 per mbf (figure 6).

Net present value increased $0.5 million (13 percent) to

$4.4 million at a $50 stumpage price. Annual harvest

volumes increased 1.3 million board feet (10 percent) to

13.5 million.

Study Results -45-

W

«N<

e

15 . Q

12 . 5

10, 8

7 . 5

5 . 0

2-5-j

0. 9

COST RATE SENSITIUITV

NFU ( MMS >

Annual Cut < MMifef 5

II Hi i 0 0

3 0

4 b 8

S tuMpacfe Price <1972 $/whf > •"B Rate -0 1/. Rate

1 3 0

Figure 6. Sensitivity of Net Present Value and Harvest Volume to Two Cost Rates.

Optimal management regimes intensified to include more

site preparation and some initial planting. Precommercial

thinning became optimal in most habitat groups and area

classes (Appendix E.3).

4.5 Price Rate Sensitivity

The annual trend in price was tested with two rates of

increase. The optimistic rate of 3.8 percent was consistent

Study Results -46-

with U.S.D.A. Forest Service projections (run 4). A

pessimistic rate of 2.6 percent was also analyzed (run 5).

Run 4 results will be discussed first.

Over a $10 to $100 per mbf stumpage price range, net

present value increased an average of 36 percent with only a

3 percent average increase in sustainable harvest volume

(figure 7).

W

c 3

PRICE RATE SENSITIUITV 15 . 0-.

12.5-

1G . O -

7 . 5

5 . 0

2 . 5

G. 0

Annual Cut

51 MPU

< >

| 3 4 £ 8 9

0

StiiKpage Price CIS 72 ) C 2 . H a t e - B 3 . 3Y. Rate-B 3 . QmA Rate

Figure 7 . Sensitivity of Net Present Value and Harvest Volume to Selected Price Rates.

1 © O

At $50 per mbf stumpage price, the annual harvest

increased 9 percent (1.2 mmbf) to 13.4 million board feet.

Net present value jumped 35 percent ($1.4 million) to $5.2

million.

Study Results -47-

The 3.8 percent price rate was sufficient to intensify

management regimes primarily in area classes 1 and 2.

Regeneration regimes also favored site preparation in area

class 3. Yield regimes favored precommercial thinning in

most habitat groups in area classes 1 and 2 (Appendix E.4).

If annual price increases dropped to 2.6 percent, harvest

volume would remain about the same (a 4 percent average

drop), but net present value would drop to 65 percent, on

average, over the stumpage price range.

At $50 per mbf stumpage price, annual harvest volume

remained at 12.2 million board feet. Net present value

dropped $1.3 million to $2.5 million.

Management regimes changed little. Site preparation was

dropped from two habitat groups (Appendix E.5).

4.6 Discount Rate Sensitivity

The selection of discount rate is a critical assumption

in analyses of forest investments and determination of

optimal management regimes. Flowers found considerable

variation in real discount rates used by other government

agencies, ranging from 4 percent to 10 percent (Montana

Study Results -48-

Dept. of State Lands 1984).

Based upon a canvass of forest products companies, a real

after-tax rate of about 6 percent was derived for Montana's

forest products industry. For comparison purposes, an

after-tax rate of 6 percent would be equivalent to a taxable

9 yield of 6.9 percent. This rate is similar to the 7 percent

rate used by the Washington Department of Natural Resources

for forest investment analyses (Montana Dept. of State Lands

1984). Although the 7 percent rate is competitive with the

forest industry, discount rates of this magnitude are not

looked upon favorably by many foresters due to the adverse

impact on practicing intensive forestry, especially in the

northern Rocky Mountains.

DSL was reluctant to equate itself with the forest

products industry and chose a 3.3 percent rate. This rate

is consistent with DSL's unique business environment and

more accurately represents the alternative cost of capital

in state government. Therefore, the base run assumption was

3.3 percent in this study. Two rates were used in this

sensitivity analysis; 4 percent (run 6) and 5 percent (run

7).

9. Taxable Yield = After Tax Yield * Capital Gains Taxable Rate / (1 - Tax Rate); .06 * .6 / (1 - .48) = 6.92%.

Study Results -49-

The 4 percent discount rate lowered net present values an

average of 64 percent from the base run across the stumpage

price range (figure 8). The large net present value drop was

not matched by a similar reduction in harvest volume, which

dropped only 10 percent, on average.

DISCOUNT RATE SENS I TIU I Tk?

e •

15 , 0!

12 . 5

. 0

7 . 5

5. a

2 . 5

0.3

Annual Cut ( Mwbf 5 —~

NFU ( M Wi5- )

T-1 2

-f n •I pf ii psjz iJ pL 4* 3 4 5 6

0 e e

StuMpage Price <1972 ) 5x Rate • 4 X Rate-® 3 .3"A Rate

O

Figure 8. Sensitivity of Net Present Value and Annual Harvest Volume to Three Discount Rates.

Annual harvest volume lowered to 11.7 million board feet

(down 5 percent), but net present value lowered to $1.4

million (down 64 percent) from the levels produced at a 3.3

percent discount rate and a $50 per mbf stumpage price.

Minor changes occurred in management regimes (Appendix E.6).

Study Results -50-

The 5 percent discount rate reduced net present value and

harvest volumes even further than the 4 percent rate. Net

present values crashed by an average of 91 percent and

volumes dropped by 25 percent of the results from the 3.3

percent discount rate over the stumpage price range.

Over $3.5 million was trimmed from the 3.3 percent net

present value to $0.4 million at the $50 per mbf price.

Sustainable harvest volumes became 9.2 million board feet,

down 25 percent from 12.2 million board feet level

calculated at the lowest discount rate.

Optimal management regimes favored no site preparation

alternatives in all but the most productive and operable

habitat group (Appendix E.7).

The elasticity of marginal acres to stumpage prices was

sensitive under higher discount rates and lower prices. All

114,090 acres were supra-marginal at prices of $30 per mbf

or more. The 5.0 percent discount rate analysis resulted in

28,557 acres and 13,116 acres of submarginal timberland at

$10 and $20 stumpage prices, respectively. Using a 4.0

percent discount rate produced 4,801 acres of submarginal

timberland at a $10 price. All timberland acreage became

supra-marginal at $20 stumpage stumpage price under the 4.0

percent discount rate.

Study Results -51-

4.7 Rotation Age Sensitivity

In the base run, the objective was to maximize net

present values. Rotation age was that age in which NPV was

maximized. Generally, forest managers wish to maintain

profitable operations. But once an acceptable profit level

has been achieved, they may want to grow trees until

culmination of mean annual increment and not maximize

wealth. This analysis (run 8) answers the question: From

all profitable management regimes, which one produces the

largest sustainable harvest volume?

The regional results projected an average increase in

harvest volume of 64 percent but dropped net present values

by 46 percent of base run values over the stumpage price

range (figure 9).

At a stumpage price of $50 per mbf, net present value

dropped to $2.1 million (down 53 percent) from the $3.9

million produced in the base run. This increased

sustainable harvest levels to 21.8 million board feet, a

change of 9.6 million feet (up 79 percent).

Study Results -52-

The analysis changed optimal yield regimes from no

precommercial thinning in the base run to intensive thinning

regimes consisting of combinations of precommercial and

commercial thinning. Changes in regeneration regimes were

minor (Appendix E.8).

ROTATION AGE SENSITIVITY £5 . 0

W

e

•p o

2e. Q-l

17 . 5

15 . 0-1 •* . 5 -ie. o

7 . 5

5 . 0

2.5-1

@. 0

Annual Cut

HPU < J

Jl 1 6

4 to 7 0 @

Stuwpage Price C1972 > €Hax. CHftl-BHax. HPU

Figure 9. Sensitivity of Net Present Value and Annual Harvest Volume to Two Rotation Age Objectives.

4.8 Management Regime Sensitivity

Traditional forestry texts stress maximizing physical

yields. That is, timber is harvested at the culmination of

Study Results -53-

mean annual increment. This philosophy, still practiced and

subscribed to by many foresters, interests forest managers

and technical foresters as more emphasis is placed on

financial responsibility. This analysis (run 9)

investigates the results of maximizing mean annual

increment.

Annual harvest volumes increased 82 percent, on average,

from the base run over the stumpage price range.

Capitalized costs soared to a constant $4.7 million,

dropping average net present value to 156 percent of the

base run (figure 10).

MAH. MAI SENSITIUETV

•ri 1°

3

25 . G1 20. 0 15 . 0. 10. 0

5 . 0-fll 4J a

-5 .

y *

o -ie. © -15 . 0 — 20. o--25 . o

Annual Cut CHwfcf>

-f-yT laT _p

NPU •C 31

1 a 2 0

3 &

5 a

6 7 8 9

StuMpasre Price C1972 •iHax. Mfll-SMax. NPU

Figure 10. Sensitivity of Net Present Value and Annual Harvest Volume to Two Management Objectives.

1 O e

Study Results -54-

Specifically, an annual harvest of 23.9 million board

feet produced a capitalized cost of $4.7 million for a

negative $1.0 million in net present value at a $50 stumpage

price. About 78,060 acres of timberland became submarginal,

suggesting another use of the land might be in order (figure

SA *3 C rS

Ul in sS & ©

«?8?

125

JL©0-

75 •

25

Q

MftM. MAI MARGINAL ACREAGE

n (l ii jiii

2 3

HI

@ e 6 o

© e 9 e

S t impasre Price <1972 USUB-HARG DSUPRA-MftRG

1 e

Figure 11. Sensitivity of Marginal Acreage to Two Management Objectives.

From the traditional forester's perspective, things

couldn't be better. Optimal regeneration regimes consisted

of intensively preparing and planting most areas. Yield

Study Results -55-

regimes included precommercial and commercial thinning on

the lower productive habitat groups. Rotation ages ranged

from 129 years to 146 years (Appendix E.9).

Unfortunately for the company following traditional

forestry objectives, financial bankruptcy will follow in

short order. From a government agency standpoint, the

taxpayers will be required to subsidize an agency which is

capable of not only sustaining itself but generating profits

for other uses.

4.9 Regeneration Policy Sensitivity

Many organizations establish a policy requiring that

regeneration of cutover timberland be promptly reforested.

In this analysis (run 10) an assumption to reforest

timberland within ten years after harvesting was simulated.

To effect the regeneration policy, time delay and success

probabilities were changed from the base run parameters.

Initial regeneration delays greater than ten years were

changed to ten years with corresponding decreases in success

probabilities. The data were provided by the SWLO

silviculturist.

Study Results -56-

Over the $10 to $100 stumpage price range, the regional

results did not increase annual harvest volumes, on average,

but net present value was reduced by 20 percent (figure 12).

REFORES T . POL I C¥ S ENS ITIUI T

W

•M c a

2 0 . 0

4 -7 J. I. 5

15 . 0

12 . 5

18. 0

7 . 5

5

2

@

Q

Annual Cut < bu*ih£ >

=•_ I i G

•O

e 3 m 4

e 7 0 @

9 &

£tuMpaore Price <1972 S/uLf>

1 O ©

KTiflf "Ill' i l l 1© 'ir 3>e 1 ay -Q Mo Lis it

Figure 12. Sensitivity of Net Present Value and Annual Harvest Volume to Two Regeneration Delays

At a $50 stumpage price, prompt reforestation increased

annual harvest volumes by 5 percent (up 0.6 million board

feet) to 12.8 million board feet. Nevertheless, net present

value dropped 15 percent (down $0.6 million) to $3.3 million

because of the policy-induced reforestation requirement.

Study Results -57-

Regeneration regimes included more site preparation and

artificial regeneration (planting and interplanting) as

subsequent treatments. Natural regeneration dominated the

initial reforestation treatment category.

A.10 TIMLAN Sensitivity

TIMLAN analyzes the capital budgeting question somewhat

differently than the SUPPLY analyses. TIMLAN escalates

lumber prices and conversion costs, then derives stumpage

prices as a residual value. This procedure guards against

projecting raw material prices at levels higher than

finished goods prices.

Comparing the TIMLAN results (run 11) to the base run

results at $50 stumpage price indicated net present value

increased $6.6 million (up 170 percent) to $10.5 million by

expending $2.3 million; a 169 percent increase in present

cost.

Despite the increase in net present value, the efficiency

of producing profit did not change. More importantly,

efficiency did not decline, and it would be advantageous to

produce at the higher profit level. The operating return on

Study Results -58-

10 sales remained unchanged at 82 percent.

Annual harvest volume increased 2.6 million board feet

(up 21 percent) to 14.8 million board feet from the base run

harvest of 12.2 million. The increase was due to longer

rotations, higher mean annual increments and more intensive

management regimes (table 10).

Management regime intensity increased in each area class

over base run regimes. Optimal reforestation regimes

included more site preparation and artificial reforestation

methods, such as initial and subsequent planting and

interplanting. Yield regimes included more precommercial

and commercial thinning.

TIMLAN results are considerably more favorable than those

of the base run. Not only did organizational wealth (NPV)

increase, but so did harvest levels and management

intensity.

Price and cost rates were not changed from the base run.

In effect, lumber prices were increasing at a rate equal to

base run stumpage prices. If the rate of increase in lumber

prices was less than the base run rate, then the results

10. Operating ROS = Net present value / Discounted total revenue; base run: $3871/$4709 = 82%, run 11: $10456/$12707 = 82%. Dollars in thousands.

Study Results -59-

would be less liberal.

Table 10

TIMLAN Optimal Management Results

HB REGEN YLD ROT HAR NPV MAI MED GR REG. REG AGE AGE /AC /AC DIA NO NO. NO. (Yr) (Yr) ( $ ) (bf) (In)

Area Class 1 (46, 843 ac • )

2 7 2 55 50 237.81 132 12.3 3 7 1 87 80 79.88 128 11.0 4 7 5 97 90 99.43 133 12.6 5 8 2 65 50 158.80 117 13.3

Area Class 2 (28, 344 ac . )

2 10 2 63 60 255.22 168 12.8 3 7 1 94 80 62.83 119 11.0 4 3 5 105 90 75.91 123 12.6 5 10 2 59 50 198.85 129 13.3

Area Class 3 (23, 548 ac . )

2 8 1 55 50 199.31 126 12.3 3 7 1 88 80 63.61 127 11.0 4 7 5 98 90 74.58 132 12.6 5 8 2 65 50 130.31 117 13.3

Area Class 4 (15, 355 ac • )

2 10 2 63 60 190.76 116 12.3 3 7 1 94 80 50.34 115 11.0 4 3 5 105 90 59.77 96 11.9 5 10 2 59 50 114.76 112 13.2

Note: Column titles are defined in table 8 on page 36.

Chapter 5

Discussion and Conclusion

Three computer-run models, PROGNOSIS, SUPPLY and TIMLAN,

were used to answer four questions often asked by managers

of forest organizations. These qustions were:

- What combinations of forest management practices will optimize the organization's objectives?

- Given the optimal combinations of practices, what is the sustained timber output level?

Will the added investments increase the organization's wealth. That is, will the organization be better off to make those investments?

- How do policy decisions affect the organization's wealth?

The base run (run 1) provided the analyst and manager

with financially optimal management regimes for a defined

set of physical (timberland) characteristics, management

characteristics and a range of probable management regimes.

Following the determination of optimal regimes, the

sustained harvest schedule was determined. A key assumption

-60-

Discussion and Conclusion -61-

was that sustained harvest should equal the mean annual

growth increment (defined as Scribner board foot volume in

this study) produced by the financially optimum management

regimes.

Finally, by testing the sensitivity of certain inputs, it

was possible to analyze the elasticity of the results to

economic and policy changes. Parameters causing important

deviations in key results were identified, allowing closer

scrutiny of those characteristics.

Only bare land values were analyzed. It was assumed that

the manager was starting with bare land after a previous

final harvest. In addition, the use of discounting formulae

for an infinite time series assumed, by definition, that the

land would continue to produce timber crops; alternative

uses were not directly analyzed.

Existing, standing timber was not included in this

study. Other methods and models are available to the

analyst for modeling residual timber. Although, it can be

argued that, from a long-term, sustained harvest standpoint,

the appropriate annual harvest should equal the mean annual

growth of the forest under the prescribed management

regimes.


The models were extremely versatile and powerful,

allowing the analyst to manipulate, simulate and assimilate

many combinations of parameters. Because of the power and

versatility of the models, the analyst should approach their

use with experience and judgment.

Some enhancements to the models would increase user

utility and efficiency. PROGNOSIS needs a back-end program

module which will aggregate stand outputs into an averaged

report. The SUPPLY program needs a front-end module which

would read PROGNOSIS output directly.

5.1 Impressions of PROGNOSIS

PROGNOSIS provides the analyst and manager with a tool

for projecting stand yields. Overall, the yield results

appeared reasonable. Two exceptions surfaced, however.

First, the difference between managed and natural yields did

not seem as responsive as anticipated. For example,

precommercial thinning yields did not differentiate from no

thinning as greatly as expected. Natural stand yields

appeared too high, whereas, managed stand yields seemed

reasonable.


Second, board foot yields from low productivity sites

were too optimistic. Habitat group 5 produced volume yields

greater than more productive groups. This anomaly is not

consistent with empirical results and accepted theory.

Problems with PROGNOSIS may be centered in the mortality

and small tree growth functions. The new program update,

version 5.0, may adequately correct these problems.

These concerns are subjective, but are based upon the

combined experiences of seven foresters who reviewed the

PROGNOSIS results.

5.2 Impressions of SUPPLY and TIMLAN

SUPPLY and TIMLAN provide the analyst and manager with a

tool which tirelessly analyzes every cell of each input

matrix. The results are consistent with theory, but there

were a few surprises. These included the time-intensity

substitutability of management regimes. With a 3.3 percent

discount rate, optimal regimes and rotation ages were quite

variable to other assumptions. At higher discount rates,

results become more predictable as optimal regimes

approached no management.


The analyst should be aware of some possible sources of

error in SUPPLY and TIMLAN. The models have subroutines

which compute management costs and stumpage prices. These

subroutines were developed from multiple regression analyses

of Forest Service and forest products industry data up to

about 1978. These data may not reflect the situation of the

analyst's organization.

Alternative coefficients can be substituted into the

subroutines to reflect the organization's unique business

environment and perceptions of the future. Management costs

may be directly submitted by the analyst by including an

optional record in the management input file, thereby

circumventing the embedded cost algorithms. Also, annual

price and cost escalation rates may be included by including

optional records.

SUPPLY determines stumpage price in the subroutine VALUE

from a multiple regression equation developed by Jackson and

McQuillan (1979). McQuillan reports that these equations are

available for the Lolo, Bitterroot, Kootenai and eastern

National Forests. The simplified equation used in SUPPLY is

of the general form (McQuillan 1981, 88-91):


S = a +a * In D + a * C + a * T 12 3 4

+ a * L + a * B 5 6

and

C = 1 for clearcut harvest,

0 for other harvest

while

T = 1 if slope is less than 40 percent

0 otherwise

where

S = stumpage value (1972 constant dollars per mbf)

In D = natural logarithm of median diameter

L = mean lumber price index

C = proportion of area clearcut

T = proportion of area tractor logged

B = expected number of timber sale bidders

a = regression coefficients i

The equation above is first solved for the mean lumber

price index (L) in SUPPLY. The Lolo N.F. equation, which was

embedded in the program, uses 3.1 as the expected number of

timber sale bidders, 15.0 inches as the projected median


diameter of second-growth timber at harvest, 17.73 percent

of area clearcut and 59.23 percent of area tractor logged.

Once the average lumber price is solved, a specific stumpage

price is calculated for each combination of area class,

habitat group, median stand diameter, et cetera.

In TIMLAN, the subroutine TIMVAL combines with VALUE to

predict future stumpage price as a residual value from

projected lumber price minus conversion costs (McQuillan

1981, 101-102). VALUE produces a stumpage price for the

beginning year, after which, TIMVAL calculates future

stumpage prices using the following equation:

PC = LP - SP / (1 + OR)

where

PC = production cost

LP = lumber price

SP = stumpage price

OR = overrun percentage

Embedded in TIMVAL are the following assumptions

(Merzenich 1979, 38-45):

- Production costs will increase 1.29 percent per year (66.85% from 1978 to 2030) due to:

* Increases in production costs (chiefly wages) not matched by increased productivity,


* Environmental constraints increasing logging and manufacturing costs and

* Reluctance of forest products industry to accept raw material substitutes (i.e., petroleum products).

- Lumber price will increase 1.83 percent per year (95.10% from 1978 to 2030) in response to:

* Increasing demand for wood products and

* Increasing costs of production.

Conversion overrun from logs to lumber will increase about 0.27 percent per year from 31.44 percent in 1978 to 45.58 percent in 2030 due to milling efficiencies.

Should the VALUE and TIMVAL parameters be different from

the organization's data and perceptions, appropriate changes

should be made in the model to better simulate the

environment of the organization.

5.3 Selection of Stumpage Price Indexes

The analyst should also be aware that price indices

should accurately reflect the market value of the future

products. Various embedded algorithms calculate market

values for only lumber. Should the organization be

marketing significant amounts wood for other products, such

as pulp and paper, the analyst may want to modify the

program to include these other products.


In the case of lumber prices, historic species indices

reflect higher quality wood from old growth trees. For

second-growth timber, these premium indices may not be truly

accurate. This is especially true of ponderosa pine. The

lumber prices for old growth "yellow pine" is highly

inflated over the second-growth "bull pine" prices.

5.4 Strategic Forecasting and Decision Making

Forecasting technological and economic change for 50, 80,

100 or more years appears quite mystical to many people.

Especially when data bases of 20 years are an exceptional

find, rather than commonplace.

Given the wide range of variability in forests and the

difficulties of obtaining recorded data for input, the

manager should consider the model results as an optimal

strategy, rather than unyielding conclusions. More

consistent management regimes can be implemented to reach

organizational objectives. Nevertheless, the analyst and

manager must recognize that variation in individual forest

stands may warrant different management regimes to be used

on an individual stands basis. In other words, optimal

management regimes should serve as guidelines, and variation


should occur when justified.

From a strategic framework, the models assist the manager

by providing better information from which to make

decisions. The manager is better able to maneuver the

organization into the most favorable long-term position with

the information provided by the analytical procedure

presented in this paper.

Because of the forest products industry's sensitivity to

economic fluctuation, many companies face periods of

overcapacity, leading to industry shakeouts and company

reorganizations (Sonnenfeld 1981,79-83). These industry

fluctuations filter through the industry structure,

affecting stumpage prices to the smallest timberland owner.

To successfully compete in this competitive business

environment, better decision-making will become more

critical than ever before.

5.5 Budgeting Process

By projecting certain prices and costs, it was possible

to determine an annual operating budget, total revenue and


net operating income for a set of optimal management

regimes. Knowing forestry activity costs of optimal

regimes, area class acreages and the percentage that

treatments would occur, the analyst can estimate annual

capital and expense budgets. Since these data are inputs

into SUPPLY, it is a simple matter to disaggregate the total

costs into respective budget items.

5.6 Conclusion

From the analytical analysis, the following operating

guidelines are appropriate for the Southwestern Land Office

to optimize organizational wealth at a regional stumpage

price of $50 per thousand board feet.

Tractor scarification is optimal on all habitat groups

characterized by gentle slopes (40 percent or less) and a

local seed source (area class 1). On these same sites,

natural regeneration predominates as the initial and

subsequent regeneration method. Regeneration delays ranging

from five to 15 years are acceptable on the four habitat

groups. Precommercial thinning, alone or in conjunction

with later commercial thinnings, is not optimal. No

intermediate stand treatments are advised. Rotation age on

Discussion and Conclusion

l

-71-

these sites ranges from 55 years on the most productive

sites to 87 years on less productive sites.

Sites characterized by gentle slopes and no local seed

source (area class 2) should not be scarified, planted or

thinned with the exception of the highest productive sites.

Highly productive sites (habitat group 2) should be

scarified, planted when an initial natural regeneration

attempt fails and receive precommercial thinning only.

Regeneration delays of eight to twenty-five years are

acceptable in this area class. Rotation ages range from 58

years to 97 years, depending on the productivity group.

Sites characterized by steep slopes (greater than 40%)

and a local seed source (area class 3) should receive

prescribed burning on the two highest productive sites

(habitat groups 2 and 3) but no site preperation treatment

on the lower two productive sites. Natural regeneration and

no thinning predominates on all habitat groups.

Regeneration delays up to 19 years, inclusive, are

acceptable. Rotation age ranges from 56 years to 79 years.

Sites characterized by steep slopes and no local seed

source (area class 4) should receive no site preparation

with the exception of the highest productive site, which

should be prescribed burned. Natural regeneration


predominates as the initial and subsequent method for

reforestation. No thinning is recommended on all sites in

this area class. Regeneration delays of up to 25 years,

inclusive, are acceptable. Rotation age ranges from 60

years to 97 years.

The value of the SWLO's available timber acreage will

optimize at about $3.9 million dollars and a sustained

harvest of over 12 million board feet will be achieved in

perpetuity when the optimal operating guidelines are

executed. If the assumptions, parameters and simulations

adequately reflect the business environment of the

organization, then to deviate significantly from the

guidelines will not achieve the organization's mandated

objective of producing sustained revenue.

In conclusion, the models can provide the analyst and

manager with powerful tools for simulating complex physical,

financial and management related conditions. The results

should allow better decisions to be made. The manager has

at his fingertips quantitative reports that, when combined

with his experience, judgment and perceptions, will result

in more knowledgeable decisions and success for the

organization.

-73-

Appendix A

Pertinent Assumptions

in SUPPLY and TIMLAN Models

The following list is quoted from McQuillan (1981,

255-256).

1. Silvicultural systems can be classified either as

clearcuts with up to three commercial thins or two-stage

shelterwood cuts with up to two commercial thinnings. For

clearcuts, the final removal is the regeneration harvest,

for shelterwood cuts the first stage entry is the

regeneration harvest.

2. Regeneration is binomial in form. That is, either

regeration is achieved or it is not. There are no varying

degrees of regeneratin success. Consequently, there is no

difference between a successfully regenerated naturally

seeded stand or a successful plantation.

3. For purposes of valuation, the diameter distribution of

a stand can be characterized by a single (median) diameter

Appendix A -74-

measureraent.

4. Stumpage prices [by tree diameter distributions]

applicable to National Forest timber also apply to other

ownerships.

5. In SUPPLY, the species mix is constant (homogeneous)

throughout a forest. In TIMLAN, the species mix does not

vary within a habitat group in any one area class.

6. Most, but not all, potential users seek to maximize net

present value of an infinite series of equal length

rotations.

7. Volume yields projected by decade provide sufficient

resolution for most management and planning applications.

8. Between any two partial removals, or between a partial

removal and the final harvest, there is no more than one

turning point in the graph of land expectation value [NPV]

against time. That is, land expectation value behaves

monotonically with respect to time.

-75-

Appendix B

SWLO Commercial Timberland Acres By Area and Habitat Group (In Thousand Acres)

HB GR YLD HABITAT COMMERCIAL WATER AVAILABLE NO CAT TYPE FOREST INFLUENCE TIMBER

1 II ABLA/CLUN 1.48 1. 48 0.14 0 .14 1.34 1 .34

2 II ABGR/CLUN 2.71 0.26 2.45 ABGR/LIBO 4.86 0.47 4.39

ABGR climax 0.37 0.04 0.33 THPL/CLUN 0.50 0.05 0.45

PICEA series 2.66 11 .10 0.26 1 .08 2.40 10 .02

3 III ABLA/XETE 8.63 0.83 7.80 ABLA/VACA 0.33 0.03 0.30 ABLA/LIBO 1.18 0.11 1.07 ABLA/MEFE 1.72 0.16 1.56 ABLA/VAGL 0.95 0.09 0.86 ABLA/CACA 0.47 13 .28 0.04 1 .26 0.43 12 .02

4 III PSME/VACA 2.68 0.26 2.42 PSME/PHMA 24.72 2.38 22.34 PSME/LIBO 8.87 0.85 8.02 PSME/SYAL 26.61 2.56 24.05 PSME/CARU 16.14 1.55 14.59 PSME/VAGL 4.84 0.47 4.37 PSME/CAGE 1.32 0.13 1 .19 PSME/ARUV 0.92 0.09 0.83

PSME climax 1 .52 87 .62 0.15 8 .44 1.37 79

o

00 •


Appendix B -76-

Appendix B Continued

HB GR YLD HABITAT COMMERCIAL WATER AVAILABLE NO CAT TYPE FOREST INFLUENCE TIMBER

5 IV ABLA/LUHI 0.33 0. 33 0.03 0. 03 0.30 0 .30

6 V PSME/FESC 1.14 0.11 1.03 PSME/AGSP 5.01 0.49 4.52 PIPO/SYAL 2.19 0.21 1.98 PIPO/FEID 2.43 0.23 2.20 PIPO/AGSP 0.26 0.03 0.23 PIPO/PUTR 0.05 0.00 0.05

— — _ _ PIPO/PRVI 1.35 12. 43 0.13 1. 20 1.22 11 .23

7 N/A SCREE 1. 26 0.12 1.14 Hrdwd. climax 1.30 2. 56 0.13 0. 25 1.17 2 .31

AREA CLASS TOTALS: 128.80 128.80 12.40 12.40 116.40 116.40

Source: DSL, Forestry Division. 1982. "Forest Statistics for Land Administered by the Southwestern Area Land Office." Various Tables.

Note: Some data derived from source.

-77-

Appendix C

Habitat Group Yield Response

to Selected Yield Regimes

The following four pages contain graphs of the Scribner

board foot volume and median stand diameter response to the

yield regimes. Graphs of median stand diameter response

show no precommercial thinning (control) and precommercial

thinning diameter responses for clarity. SV6 means Scribner

board foot volume to a 6 inch diameter tree top.

Appendix C -78-

m BBS* asm

U Urn

W

£.

s»* A H

20. 01 17 . 5

15. 0

12 . 5

10. 0

7. 5

5. 0

2 « 5

0. 0

HABITAT GROUP 2 REGIMES

•r

Jgf J£T

3-2s 5e 75 h lo

**5 30

STAND AGE CYEARS? • 1 & 2

•40. 0 n

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Vi -a a Q J J » - w i

31 £ p=c n :

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STAND AGE C YEARS> JL - 2 — 3 - 4 : 5 — f>

Appendix C -79-

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12 . 5 B~S U 10. 0 d. M W 7. 5

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5 . 0

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HABITAT GROUP 3

& JBr*& M'

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STAND AGE (YEARS> • 1 2

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STAND AGE CYEARS> 1 2 — 3 4 - 5 —

Appendix C -80-

<i W H S u £ M W

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Appendix C -81-

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STAND AGE CYEARS>

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STAND AGE (YEARS) j_— 2 — 3 — 4— 5— 6

Appendix D

Base Run Results From $10 to $100 per mbf Stumpage Price Range

Column Heading Definitions

STUMP PRICE means stumpage price in dollars per thousand board feet.

REGEN REG. NO. means regeneration regime code number.

YLD REG NO. means yield regime code number.

ROT AGE means rotation age in years.

HAR AGE means harvest age.

NPV/AC means net present value per acre.

MAI/AC means mean annual increment per acre, measured board feet.

Appendix D -83-

D.l Habitat Group 2 Results


($/rabf) NO. NO. (Yr) (Yr) ( $ ) (bf)

Habitat Group 2, Area Class 1 (4, 762 ac.)

10 7 1 65 60 30.44 150 20 7 1 55 50 44.10 126 30 7 1 55 50 58.08 126 40 7 1 55 50 72.07 126 50 7 1 55 50 86.05 126 60 7 1 55 50 100.04 126 70 7 1 55 50 114.02 126 80 7 1 65 60 128.42 163 90 7 1 65 60 143.00 163 100 7 1 65 60 157.57 163


10 1 1 73 60 25.71 134 20 1 1 73 60 35.78 134 30 7 1 60 50 46.61 116 40 7 1 60 50 58.16 116 50 8 2 58 50 69.73 120 60 8 2 68 60 82.34 155 70 8 2 68 60 95.40 155 80 8 2 68 60 108.45 155 90 10 2 63 60 122.83 168 100 10 2 63 60 138.53 168


Appendix D -84-


($/mbf) NO. NO. (Yr) (Yr) ($) (bf)

Habitat Group 2, Area Class 3 (1,190 ac.)

10 1 1 89 80 9.91 189 20 1 1 69 60 21.73 141 30 1 1 69 60 33.36 141 40 7 1 56 50 45.42 124 50 7 1 56 50 58.87 124 60 7 1 56 50 72.32 124 70 7 1 56 50 85.78 124 80 7 1 56 50 99.23 124 90 7 1 56 50 112.83 124 100 7 1 56 50 139.58 124

Habitat Group 2, Area Class 4 (1,

/•~N

•

o

CO

00 o

10 1 1 93 80 8.24 181 20 1 1 73 60 18.39 134 30 1 1 73 60 28.45 134 40 1 1 73 60 38.52 134 50 7 1 60 50 49.39 116 60 7 1 60 50 60.94 116 70 8 1 58 50 72.75 120 80 8 1 58 50 85.21 120 90 8 1 58 50 97.66 120 100 8 1 58 50 110.12 120

Appendix D -85-

D.2 Habitat Group 3 Results


($/mbf) NO. NO. (Yr) (Yr) ( $ ) (bf)


10 1 1 103 90 9.82 145 20 1 1 93 80 15.42 120 30 7 1 87 80 22.03 128 40 7 1 87 80 29.07 128 50 7 1 87 80 36.11 128 60 7 1 87 80 43.14 128 70 7 1 87 80 50.18 128 80 7 1 87 80 57.22 128 90 7 1 87 80 64.25 128 100 7 1 87 80 71 .29 128


10 1 1 107 90 8.19 140 20 1 1 97 80 13.06 115 30 1 1 97 80 18.06 115 40 1 1 97 80 23.06 115 50 1 1 97 80 28.06 115 60 1 1 97 80 38.05 115 70 1 1 97 80 43.40 115 80 7 1 94 80 48.93 119 90 7 1 94 80 54.47 119 100 7 1 94 80 60.00 119


Appendix D -86-


($/mbf) NO. NO. (Yr) (Yr) ($) (bf)


10 1 1 123 110 1.14 180 20 1 1 103 90 5.83 145 30 1 1 103 90 11.31 145 40 1 1 93 80 16.98 120 50 7 1 93 80 22.71 120 60 7 1 88 80 29.35 127 70 7 1 88 80 36.15 127 80 7 1 88 80 42.95 127 90 7 1 88 80 49.74 127 100 7 1 88 80 56.54 127


10 1 1 127 110 0.61 175 20 1 1 107 90 4.70 140 30 1 1 107 90 9.49 140 40 1 1 97 80 14.42 115 50 1 1 97 80 19.42 115 60 1 1 97 80 24.42 115 70 1 1 97 80 29.42 115 80 1 1 97 80 34.42 115 90 1 1 97 80 39.42 115 100 7 1 94 80 44.58 119

Appendix D -87-



($/mbf) NO. NO. (Yr) (Yr) ( $ ) (bf)

Habitat Group 4, Area Class 1 (36, 030 ac. )

10 1 1 103 90 11.62 118 20 1 1 93 80 16.27 100 30 7 1 87 80 21.88 107 40 7 1 87 80 27.72 107 50 7 1 87 80 33.57 107 60 7 1 87 80 39.41 107 70 7 2 87 80 45.78 119 80 7 2 87 80 52.32 119 90 7 2 87 80 58.85 119 100 7 5 97 90 66.32 133

Habitat Sroup 4, Area Class 2 (19, 401 ac. )

10 1 1 97 90 9.77 114 20 1 1 97 80 13.80 96 30 1 1 97 80 17.95 96 40 1 1 97 80 22.10 96 50 1 1 97 80 26.25 96 60 1 1 97 80 30.41 96 70 3 2 95 80 35.22 109 80 3 2 95 80 40.19 109 90 3 5 105 90 45.25 123 100 3 5 105 90 51.03 123


Appendix D -88-


($/mbf) NO. NO. (Yr) (Yr) ($) (bf)

Habitat Group 4, Area Class 3 (15 ,441 ac.)

10 1 1 103 90 3.93 118 20 1 1 103 90 8.39 118 30 1 1 103 90 12.84 118 40 1 1 93 80 17.56 100 50 1 1 93 80 22.32 100 60 7 1 88 80 27.73 105 70 7 1 88 80 33.38 105 80 7 1 88 80 39.03 105 90 7 1 88 80 44.68 105 100 7 1 88 80 50.32 105


10 1 1 107 90 3.04 114 20 1 1 107 90 6.94 114 30 1 1 107 90 10.83 114 40 1 1 97 80 14.93 96 50 1 1 97 80 19.08 96 60 1 1 97 80 23.23 96 70 1 1 97 80 27.39 96 80 3 1 95 80 31.59 98 90 3 1 95 80 36.03 98 100 3 1 95 80 40.48 98

Appendix D -89-



($/mbf) NO. NO. (Yr) (Yr) ($) (bf)


10 1 1 81 60 23.62 118 20 3 1 79 60 31.14 121 30 3 1 79 60 39.10 121 40 3 1 79 60 47.06 121 50 8 2 75 60 56.00 127 60 8 2 65 50 66.43 117 70 8 2 65 50 76.90 117 80 8 2 65 50 87.36 117 90 8 2 65 50 97.82 117 100 8 2 65 50 108.29 117


10 3 1 85 60 18.44 112 20 3 1 85 60 24.90 112 30 3 1 85 60 31.36 112 40 3 1 85 60 37.81 112 50 3 1 85 60 44.27 112 60 3 2 75 50 51.22 101 70 3 2 75 50 58.50 101 80 3 2 75 50 65.79 101 90 10 2 59 50 74.00 129 100 10 2 59 50 87.11 129


Appendix D -90-


($/mbf) NO. NO. (Yr) (Yr) ($) (bf)


10 1 1 101 80 11.39 137 20 1 1 101 80 16.82 137 30 1 1 81 60 25.64 118 40 3 1 79 60 33.31 121 50 3 1 79 60 41 .27 121 60 3 1 79 60 49.23 121 70 8 1 75 60 58.16 127 80 8 1 65 50 67.86 110 90 8 2 65 50 78.08 117 100 8 2 65 50 88.55 117


10 1 1 109 80 7.96 127 20 3 1 105 80 12.66 132 30 3 1 85 60 20.20 112 40 3 1 85 60 26.66 112 50 3 1 85 60 33.12 112 60 3 1 85 60 39.57 112 70 3 1 85 60 46.03 112 80 3 1 85 60 52 .00 112 90 3 2 75 50 59.58 101 100 3 2 75 50 66.86 101

-91-

Appendix E

Base and Sensitivity Run Results At $50 per mbf Stumpage Price

a. HB GR NO means habitat group number.

b. REGEN REG. NO. means regeneration regime code number.

c. YLD REG NO. means yield regime code number.

d. ROT AGE means rotation age.

e. HAR AGE means harvest age.

f. NPV/AC means net present value per acre.

g. MAI/AC means mean annual increment in board feet.

Appendix E -92-

E. 1 Run 1 Results

HB REGEN YLD ROT HAR NPV MAI GR REG. REG AGE AGE /AC /AC NO NO. NO. (Yr) (Yr) ($) (bf)

[a] [b] [c] [d] [e] [f] [g]


2 7 1 55 50 86.05 126 3 7 1 87 80 36.11 128 4 7 1 87 80 33.57 107 5 8 1 75 60 56.00 127


2 8 1 58 50 69.73 120 3 1 1 97 80 28.06 115 4 1 1 97 80 26.25 96 5 3 1 85 60 44.27 112

Area Class 3 (23,548 ac. )

2 7 1 56 50 58.87 124 3 1 1 93 80 22.71 120 4 1 1 93 80 22.73 100 5 3 1 79 60 49.23 121


2 7 1 60 50 49.39 116 3 1 1 97 80 19.42 115 4 1 1 97 80 19.08 96 5 3 1 85 60 33.12 112

Appendix E -93-

E.2 Run 2 Results

HB REGEN YLD ROT HAR NPV MAI GR REG. REG AGE AGE /AC /AC NO NO. NO. (Yr) (Yr) ( $ ) (bf)

Area Class 1 (46, 843 ac . )

2 1 1 59 50 18.86 118 3 1 1 93 80 7.10 120 4 1 1 93 80 6.63 100 5 1 1 81 60 12.59 118

Area Class 2 (28, 344 ac . )

2 1 1 63 50 15.93 110 3 1 1 97 80 5.93 115 4 1 1 97 80 5.52 96 5 3 1 85 60 9.54 112

Area Class 3 (23, 548 ac . )

2 1 1 59 50 13.21 118 3 1 1 93 80 4.40 120 4 1 1 93 80 4.39 100 5 1 1 81 60 9.09 118

Area Class 4 (15, 355 ac . )

2 1 1 63 50 11.06 110 3 1 1 97 80 3.58 115 4 1 1 97 80 3.56 96 5 3 1 85 60 6.50 112

Appendix E -94-

E. 3 Run 3 Results



2 7 2 65 60 92. 49 163 3 7 1 87 80 39. 82 128 4 7 2 87 80 38. 80 119 5 8 2 65 50 64. 18 117


2 10 2 63 60 80. 65 168 3 7 1 94 80 30. 35 119 4 3 2 95 80 29. 75 109 5 10 2 59 50 50. 88 129


2 7 1 56 50 63. 55 124 3 7 1 88 80 26. 37 127 4 7 1 88 80 25. 80 105 5 8 1 75 60 46. 19 127


2 8 1 58 50 54. 79 120 3 1 1 97 80 21. 12 115 4 3 1 95 80 20. 84 98 5 3 1 85 60 35. 61 112

Appendix E

E.4 Run 4 Results



2 7 2 65 50 113. 25 163 3 7 1 87 80 48. 55 128 4 7 2 87 80 45. 89 119 5 8 2 75 60 76. 97 117


2 8 2 68 60 94.' 87 155 3 1 1 97 80 36. 90 115 4 3 2 95 80 35. 30 109 5 3 2 75 50 58. 55 101


2 7 1 56 50 78. 21 124 3 7 1 88 80 31. 37 127 4 7 1 88 80 30. 71 105 5 8 1 75 60 55. 13 127


2 7 1 60 50 65. 99 116 3 1 1 97 80 25. 91 115 4 1 1 97 80 25.42 96 5 3 1 85 60 43.90 112

Appendix E

E.5 Run 5 Results


Area Class 1 (46, 843 ac . )

2 7 1 55 50 57.87 126 3 7 1 87 80 22.97 128 4 7 1 87 80 21.24 107 5 3 1 79 60 37.03 121

Area Class 2 (28, 344 ac • )

2 7 1 60 50 46.44 116 3 1 1 97 80 18. 72 115 4 1 1 97 80 17.50 96 5 3 1 85 60 29.67 112

Area Class 3 (23, 548 ac . )

2 1 1 59 50 38.94 118 3 1 1 93 80 14.87 120 4 1 1 93 80 14.66 100 5 1 1 81 60 27.40 118

Area Class 4 (15, 355 ac • )

2 1 1 63 50 33.06 110 3 1 1 97 80 12.58 115 4 1 1 97 80 12.39 96 5 3 1 85 60 21. 74 112

Appendix E -97-

E.6 Run 6 Results


Area Class 1 (46, 843 ac . )

2 7 1 55 50 38.38 126 3 7 1 77 70 12.11 101 4 7 1 87 80 10.89 107 5 3 1 69 50 22.74 104

Area Class 2 (28, 344 ac . )

2 7 1 60 50 29.85 116 3 1 1 87 70 9.60 89 4 1 1 97 80 8.81 95 5 3 1 75 50 17.44 95

Area Class 3 (23, 548 ac . )

2 7 1 56 50 25.55 124 3 1 1 83 70 7.79 93 4 1 1 93 80 7.62 100 5 3 1 69 50 16.44 104

Area Class 4 (15, 355 ac . )

2 1 1 63 50 21.14 110 3 1 1 87 70 6.34 89 4 1 1 97 80 6.21 96 5 3 1 75 50 12.53 95

Appendix E -98-

E.7 Run 7 Results



2 7 1 45 40 13. 13 101 3 1 1 83 70 2. 78 93 4 1 1 83 70 2. 46 75 5 3 1 69 50 6. 59 104


2 1 1 53 40 9. 62 86 3 1 1 87 70 2. 11 89 4 1 1 87 70 1. 85 72 5 3 1 75 50 4. 70 95


2 1 1 49 40 8. 60 93 3 1 1 83 70 1. 72 93 4 1 1 83 70 1. 61 75 5 3 1 69 50 4. 63 104


2 1 1 53 40 6. 78 86 3 1 1 87 70 1. 25 89 4 1 1 87 70 1. 15 72 5 3 1 75 50 3. 25 95

Appendix E

E.8 Run 8 Results


Area Class 1 (46, 843 ac . )

2 7 4 95 90 50.77 209 3 1 4 123 110 13.94 126 4 7 5 147 140 19.91 202 5 8 5 135 120 29.83 189

Area Class 2 (28,344 ac • )

2 8 4 98 90 39.75 203 3 1 4 127 110 11 .82 122 4 1 5 157 140 16.67 189 5 3 5 165 140 20.32 177

Area Class 3 (23, 548 ac • )

2 1 4 99 90 28.62 201 3 1 1 153 140 8.91 210 4 1 5 153 140 9.74 194 5 8 5 135 120 16.95 189

Area Class 4 (15,355 ac . )

2 8 4 98 90 19.43 203 3 1 1 157 140 7.43 206 4 1 5 157 140 8.16 189 5 3 5 165 140 12.43 177

Appendix E -100-

E. 9 Run 9 Results


Area Class 1 (46, 843 ac . )

2 10 1 143 140 -7. 60 250 3 9 1 146 140 -8. 70 220 4 8 5 146 140 13. 88 203 5 10 5 129 120 -5. 64 198

Area Class 2 (28, 344 ac • )

2 10 1 143 140 -7. 60 250 3 10 1 146 140 -23. 03 220 4 10 5 146 140 -16. 14 203 5 10 5 129 120 -5. 64 198

Area Class 3 (23, 548 ac . )

2 10 1 143 140 -13. 93 250 3 9 1 146 140 -13. 90 220 4 10 5 146 140 -28. 65 203 5 10 5 129 120 -21. 27 198

Area Class 4 (15, 355 ac . )

2 10 1 143 140 -13. 93 250 3 10 1 146 140 -28. 24 220 4 10 5 146 140 -28. 65 203 5 10 5 129 120 -21. 27 198

Appendix E -101-

E.10 Run 10 Results



2 7 1 55 50 82. 00 126 3 7 1 88 80 30. 31 126 4 7 1 87 80 29. 75 107 5 7 1 79 60 41. 03 121

Area Class 2 (28, 344 ac . )

2 8 1 58 50 69. 73 120 3 3 1 99 80 14. 85 113 4 3 1 96 80 17. 38 97 5 9 1 69 60 55. 00 138


2 7 1 56 50 54. 97 124 3 7 1 89 80 17. 35 125 4 7 1 88 80 18. 40 105 5 3 1 81 60 27. 01 118


2 7 1 59 50 48. 35 118 3 3 1 99 80 6. 78 113 4 3 1 96 80 9. 96 97 5 3 1 84 60 20. 86 114

-102-

Appendix F

Bibliography

Anderson, D.L., and G.R. Schaertl. 1983. The forest products industry: A case analysis. Mgmt 656 Report. University of Montana, Missoula. 20.

Baumol, W.J. and A.S. Blinder. 1982. Economics: Principles and Policy. 2d ed. New York: Harcourt Brace Jovanovich, 836.

Blackman, T., ed. 1984. Housing starts tumble; bad weather blamed. Forest Industries lll(May): 7.

Gray, J. and K.S. Johnston. 1977. Accounting and Management Action. 2d ed. New York: McGraw-Hill, 749.

Jackson, D.H. and A.G. McQuillan. 1979. Montana Timber Supply: A long run model and analysis of alternative approaches. Montana Forest and Conservation Experiment Station, Missoula. Photocopy. 69.

Faustmann, M. 1849. Calculations of the value which forest land and immature stands possess for forestry. Translated from the German by W. Linnard and M. Gane. Oxford Institute Paper 42. 55.

Long, B.L. 1984. Supervisor of Forest Inventory, Montana Dept. of State Lands. Inverview with author. January through March.

McQuillan, A.G. 1981. Evaluating timberland allocation and management intensity. Ph.D. diss., University of Montana, Missoula. 271.

Makridakis, S.G. and S.C. Wheelwright. 1978. Forecasting: Methods and Applications. New York: John Wiley & Sons. 717.

Bibliography -103-

Merzenich, J.P. 1979. Classifying forest land based upon its timber management investment potential: A case study of the Lolo National Forest. Montana Forest and Conservation Exp. Sta., University of Montana, Missoula. Bull. 42. 146.

Montana Department of State Lands, Forestry Division. 1982. Forest statistics for land administered by the Southwestern Area Land Office. By B.L. Long. Photocopy. 76.

. 1984. The proposed discount rate for evaluating Montana Division of Forestry's long term timber investments. By P. Flowers. Internal memorandum. 9.

' Office of State Forester. 1970. Laws of Montana relating to forestry. Dept. of State Lands, Missoula. 53.

National Forest Products Association. 1980. The need to evaluate economically optimum timber management regimes in the planning process. NFPA, Washington D.C. Mimeo. 5.

Pfister, R.D., B.L. Kovalchik, S.F. Arno, and R.C. Presby. _1977. Forest habitat types of Montana. USDA For. Serv. , Intermountain For. and Rnge. Exp. Sta., Gen. Tech. Rep. INT-34. 174.

Remington, D. 1984. Timber Stand Improvement Forester, Montana Dept. of State Lands. Interview with author. January through March.

Sonnenfeld, J.A. 1981. Corporate Views of the Public Interest: Perceptions of the Forest Products Industry. Boston: Auburn House. 285.

U.S.D.A. Forest Service. 1982. An Analysis of the Timber Situation in the United States. Forest Res. Pap. No. 23. Washington D.C.: Government Printing Office. 499.

Western Wood Products Association. 1984. Western Lumber Price Index. Published monthly. Portland, OR. 2.

Wykoff, W.R., N.L. Crookston, and A.R. Stage. 1982. User's guide to the stand prognosis model. USDA, For. Serv., Intermountain For. and Rnge. Exp. Sta., Gen. Tech. Rep. INT-133. 112.

production capacity and capital budgeting for state forest

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